A high dynamic range (HDR) image may be created by capturing a sequence of video frames of an object in a scene, each frame at a different light exposure. The result may be a sequence of bright, medium, and dark video frames, based on the amounts of light exposure. A process to combine images from the video frames, such as juxtaposing the same object's shape in the frames of the sequence, reveals details in the shadows and highlights of the object.
High dynamic range (HDR) is useful for still portraits in controlled lighting conditions. However, for monitoring moving vehicular traffic conditions and counting cars, for example, HDR makes detecting any moving objects in an image more difficult. In the case of traffic counting, HDR increases the chance of a blurry photo. Since HDR captures multiple images, the object moves between the first and second image, and thus the composite of those images will not have crisp edges. Crisp edges usually found in high contrast scenes, are better for object recognition. But, HDR will decrease contrast and therefore results in reduced object recognition success. Moreover, if the images are captured when it is too dark or too light, the object colors are washed out with HDR. HDR makes object recognition more difficult when, for example, a vivid red automobile drives by at night with bright headlights.